---
title: "Downloading from KEGG"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Downloading from KEGG}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

<!-- generated from kegg-source.Rmd.source.  Please edit that file! -->




```r
library(volcalc)
library(dplyr) #for left_join()
#> 
#> Attaching package: 'dplyr'
#> The following object is masked from 'package:ChemmineR':
#> 
#>     groups
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
```

The `volcalc` package can be used to download .mol files directly from KEGG given either compound IDs or pathway IDs.

First, choose a directory to download files to.  For this vignette, we will use a temporary directory, but you should choose somewhere in your project.


```r
dl_path <- tempdir()
```

### Single compound usage

You can search KEGG for compunds at <https://www.genome.jp/kegg/compound/> to find their KEGG IDs starting with a "C".

Let's download .mol files for two compounds, jasmonic acid and methyl jasmonate, with KEGG IDs [C08491](https://www.kegg.jp/entry/C08491) and [C11512](https://www.kegg.jp/entry/C11512), respectively, using the `volcalc` function `get_mol_kegg()`. 


```r
mols <- get_mol_kegg(compound_ids = c("C08491", "C11512"), dir = dl_path)
mols
#> # A tibble: 2 × 2
#>   compound_id mol_path                                                              
#>   <chr>       <fs::path>                                                            
#> 1 C08491      /var/folders/wr/by_lst2d2fngf67mknmgf4340000gn/T/RtmpKwerlt/C08491.mol
#> 2 C11512      /var/folders/wr/by_lst2d2fngf67mknmgf4340000gn/T/RtmpKwerlt/C11512.mol
```

The data frame returned by `get_mol_kegg()` contains the paths the files were downloaded to in `mol_path`, making for convenient passage on to the `volcalc` function `calc_vol()`.


```r
rvi <- calc_vol(mols$mol_path)
rvi
#> # A tibble: 2 × 5
#>   mol_path                                                               formula  name                rvi category
#>   <chr>                                                                  <chr>    <chr>             <dbl> <fct>   
#> 1 /var/folders/wr/by_lst2d2fngf67mknmgf4340000gn/T/RtmpKwerlt/C08491.mol C12H18O3 (-)-Jasmonic acid  1.84 moderate
#> 2 /var/folders/wr/by_lst2d2fngf67mknmgf4340000gn/T/RtmpKwerlt/C11512.mol C13H20O3 Methyl jasmonate   3.81 high
```

`calc_vol()` also returns the file paths, so these two data frames can be easily joined.


```r
left_join(mols, rvi, by = join_by(mol_path)) %>%
  select(-mol_path)
#> # A tibble: 2 × 5
#>   compound_id formula  name                rvi category
#>   <chr>       <chr>    <chr>             <dbl> <fct>   
#> 1 C08491      C12H18O3 (-)-Jasmonic acid  1.84 moderate
#> 2 C11512      C13H20O3 Methyl jasmonate   3.81 high
```


### Pathway usage
<!-- TODO: change this example to a pathway module or smaller pathway -->

We can download single or multiple compounds with `compound_ids`, but we can also download all compounds associated with a KEGG pathway with `pathway_ids`.  Let's download the entire alpha-linolenic acid metabolism pathway ([map00592](https://www.kegg.jp/entry/map00592)) that the above two compounds are part of.


```r
alam_pathway <- get_mol_kegg(pathway_ids = "map00592", dir = dl_path)
head(alam_pathway)
#> # A tibble: 6 × 3
#>   pathway_id compound_id mol_path                                                                       
#>   <chr>      <chr>       <fs::path>                                                                     
#> 1 map00592   C00157      /var/folders/wr/by_lst2d2fngf67mknmgf4340000gn/T/RtmpKwerlt/map00592/C00157.mol
#> 2 map00592   C01226      /var/folders/wr/by_lst2d2fngf67mknmgf4340000gn/T/RtmpKwerlt/map00592/C01226.mol
#> 3 map00592   C04672      /var/folders/wr/by_lst2d2fngf67mknmgf4340000gn/T/RtmpKwerlt/map00592/C04672.mol
#> 4 map00592   C04780      /var/folders/wr/by_lst2d2fngf67mknmgf4340000gn/T/RtmpKwerlt/map00592/C04780.mol
#> 5 map00592   C04785      /var/folders/wr/by_lst2d2fngf67mknmgf4340000gn/T/RtmpKwerlt/map00592/C04785.mol
#> 6 map00592   C06427      /var/folders/wr/by_lst2d2fngf67mknmgf4340000gn/T/RtmpKwerlt/map00592/C06427.mol
dim(alam_pathway)
#> [1] 44  3
```

Notice that this returns pathway IDs and compound IDs.  We can do the same as above and pass the `mol_path` column to `calc_vol()` and then join the resulting data frame and do some basic data wrangling to find the top 10 most volatile compounds in that pathway.


```r
rvi_path <- calc_vol(alam_pathway$mol_path)
#> Warning in FUN(X[[i]], ...): Possible OpenBabel errors detected and only NAs returned.
#> Run with `validate = FALSE` to ignore this.
```



```r
left_join(alam_pathway, rvi_path, by = join_by(mol_path)) %>% 
  select(-mol_path) %>% 
  #arrange from most to least volatile
  arrange(desc(rvi)) %>% 
  #take just the top 10
  slice_head(n = 10)
#> # A tibble: 10 × 6
#>    pathway_id compound_id formula  name                         rvi category
#>    <chr>      <chr>       <chr>    <chr>                      <dbl> <fct>   
#>  1 map00592   C16310      C6H10O   3-Hexenal                   7.32 high    
#>  2 map00592   C19757      C8H14O2  (3Z)-Hex-3-en-1-yl acetate  6.75 high    
#>  3 map00592   C08492      C6H12O   3-Hexenol                   6.45 high    
#>  4 map00592   C16323      C9H14O   3,6-Nonadienal              6.05 high    
#>  5 map00592   C11512      C13H20O3 Methyl jasmonate            3.81 high    
#>  6 map00592   C16318      C13H20O3 (+)-7-Isomethyljasmonate    3.81 high    
#>  7 map00592   C16322      C9H16O3  9-Oxononanoic acid          2.77 high    
#>  8 map00592   C16343      C17H28O  Heptadecatrienal            2.69 high    
#>  9 map00592   C08491      C12H18O3 (-)-Jasmonic acid           1.84 moderate
#> 10 map00592   C16317      C12H18O3 (+)-7-Isojasmonic acid      1.84 moderate
```